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All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Initial Modeling of Urban Search Measurements

Detection of illicit nuclear materials in urban environments is difficult due to the large amount of background radiation from naturally occurring radioactive materials (NORM) in the roadways and buildings. Mobile searches suffer from low count rates, so the detection algorithms must be carefully balanced between missing real sources and reporting too many false alarms. The Modeling Urban Scenarios and Experiments (MUSE) project aims to create a virtual testbed for simulation of radiation detection to predict realistic background and threat source detection events so that detection algorithms can be better optimized to find illicit sources. The MUSE project is a collaboration of Oak Ridge National Laboratory (ORNL), Lawrence Berkeley National Laboratory (LBNL), and the Remote Sensing Laboratory (RSL). The project also works with staff at Lawrence Livermore National Laboratory (LLNL) to support the development of the Optimization Planning Tool for Urban Search (OPTUS) software package. The first step in creating a virtual testbed is to determine the level of detail required in the models to match actual measurements. Several measurement campaigns were conducted in 2015 and 2016 at the Fort Indiantown Gap (FTIG) Combined Arms Collective Traming Facility (CACTF). This facility contains a representative urban environment, with a dozen buildings and several streets. Detectors commonly used in search operations - 2 in x 4 in x 16 in NaI(Tl) - were used to measure count rates from background and sources. Measurements with a shielded high-purity germanium detector of the roadways, sidewalks, and building walls were made to determine the concentration of NORM that makes up the bulk of the background radiation. This paper shows some comparisons of modeling simulations to measurements taken during the OPTUS-3 campaign in November 2015. Measurements with and without an 81 {mu}Ci cesium source were made at several points along the center line of the main road. Initial results show good agreement between the measured and simulated spectra above 300 keV. Additional studies to determine the causes of mismatch below 300 keV have been started and will also be discussed.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2016

Unattended Sensor System With CLYC Detectors

We have developed a next-generation unattended sensor for detecting anomalous radiation sources. The system combines several technologies to reduce size and weight, increase battery lifetime, and improve decision-making capabilities. Sixteen Cs2LiYCl6:Ce (CLYC) scintillators allow for gamma-ray spectroscopy and neutron detection in the same volume. Low-power electronics for readout, high voltage bias, and digital processing reduce the total operating power to 1.3 W. Computationally efficient analysis algorithms perform spectral anomaly detection and isotope identification. When an alarm occurs, the system transmits alarm information over a cellular modem. In this paper, we describe the overall design of the unattended sensor, present characterization results, and compare the performance to stock NaI:Tl and 3He detectors.

CLYC↗
DOE OSTI2020

Minimum Detectable Quantity Calculation for Radiation Portal Monitors

The minimum detectable quantity of a radiation portal monitor is the smallest amount of radioactive material that can be detected passing through the monitor (with a specified detection probability). The minimum detectable quantity is a function of many factors, including isotope, velocity, distance between pillars, height of portal and vehicle, source distribution and position, background radiation, background suppression, detector volume and positions, detector efficiency, decision metrics and algorithms, the presence of NORM (naturally occurring radioactive material), and alarm thresholds. Experimentally testing a radiation portal monitor to failure for all combinations of these factors is extremely time-consuming and often cost prohibitive. This document outlines a straightforward method for quickly estimating the minimum detectable quantity for moving sources over a large variety of conditions with a minimal number of static measurements. The document also describes a proof of concept software application that the International Atomic Energy Agency has designed based on the described method that can be used by Member States to estimate a monitor’s minimum detectable quantity.

Blessinger, Christopher S.↗
DOE OSTI2018

Kernelized Generalized Likelihood Ratio Test for Spectrum Sensing in Cognitive Radio

Spectrum sensing in next-generation wireless radio networks is considered a key technology to overcome the problem of spectrum scarcity. Unfortunately, many approaches to spectrum sensing do not work well in low signal-to-noise ratio (SNR) environments. This paper proposes and analyzes a new algorithm named kernelized generalized likelihood ratio test (KGLRT) for spectrum sensing in cognitive radio systems to overcome this problem. Effectively, KGLRT uses a nonlinear kernel to map input data onto a high-dimensional feature space; then, the widely accepted (linear) generalized likelihood ratio test is used for hypothesis testing. This new algorithm gives a gain of 4 dB in SNR over its linear counterpart. A theoretical analysis for this algorithm is given for the first time and is shown analogous to algorithms used in image signal processing. The detection metrics are found to be concentrated random variables; furthermore, the probability distributions of the detection metrics are proved to follow the F -distributions, which agree with the results obtained using the concentration inequality. The analytical thresholds are derived for target false-alarm probabilities. Here, the thresholds are independent of noise power; thus, the proposed algorithm can overcome noise uncertainty issues at very low SNR levels. Simulations validate the theoretical results.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Comparison of Supervised and Un-Supervised Machine Learning Algorithms for Threat Detection and Scintillator Performance for Radiation Portal Monitoring

Following the events of September 11, 2001, international border crossing have been equipped with radiation portal monitors (RPMs) to identify illicit radioactive material. Polyvinyl toluene (PVT) scintillators are commonly used due to their low cost and reasonable maintainability, however they offer low spectral resolution. Despite the fact that over twenty years has transpired since this event, radioisotopes are still typically identified by hand-crafted classification algorithms, e.g., total counts or energy windowing, and exhibit relatively poor performance in detecting threats at the low false alarm rates required to support the stream of commerce. While some improvement to performance has been realized via the use of supervised machine learning, these classification algorithms typically utilize simulations in lieu of real data due to the sparsity of data for one or more classes. Accordingly, the performance of these algorithms is somewhat less than optimal when examining experiments or simulations with model mismatch. Consequently, in this work, we examine the application of a number of unsupervised machine learning, anomaly detection based algorithms, to circumvent the inverse crime when analyzing spectroscopy data for RPMs. We also compare anomaly detection results with those obtained via the use of supervised classification detection ML algorithms when model mismatch is introduced between the simulated threat items utilized for training/testing. Finally, we compared the performance of the PVT scintillators to those obtained with higher resolution detectors using both anomaly detection and supervised classification algorithms.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2020

Implementing Online Oscillation Monitoring and Forced Oscillation Source Locating at Peak Reliability

This paper introduces a framework of online oscillation monitoring systems and forced oscillation detection & source location tools that Peak Reliability ('PEAK') has implemented for the Reliability Coordinator (RC) function of the Western Interconnection. The framework consists of four main components: (1) Montana Tech's Modal Analysis Software (MAS) engine; (2) Washington State University's Oscillation Monitor System (OMS) software; (3) Forced Oscillation Detection and Source Location Algorithms (FODSL); and (4) PEAK in-house visualization tool and alarming logic built in PI Processbook. The framework has been validated and applied for real system oscillation studies successfully. Implementation experience and lessons learned will be discussed in the paper.

14 SOLAR ENERGY↗
DOE OSTI2023

A Persistence Meter for Nimble Alarming Using Ambient Synchrophasor Data

Persistent oscillations in the power grid are often indicative of fragility, and may be harbingers of systemic or cascading failures. Modernization of the grid, including increased penetration of intermittent renewables and integration of new power electronics, is making the oscillatory swing dynamics of the network both more complex and variable. In this project researchers from the University of Wisconsin-Madison (Bernard Lesieutre, lead), Washington State University (Sandip Roy, lead), and the Electric Power Group (Neeraj Nayak, lead) have developed technologies that monitor persistent oscillations in the grid and provide operators with alarms and analytics when concerning oscillations are detected. Some of the algorithms have already been implemented in EPG’s PGDA software and integrated into their RTDMS system for use in control rooms.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Effect of natural gamma background radiation on portal monitor radioisotope unmixing

It is well known that national security relies on several layers of protection. One of the most important is the traffic control at borders and ports that exploits Radiation Portal Monitors (RPMs) to detect and deter potential smuggling attempts. Most portal monitors rely on plastic scintillators to detect gamma rays. Despite their poor energy resolution, their cost effectiveness and the possibility of growing them in large sizes make them the gamma-ray detector of choice in RPMs. Unmixing algorithms applied to organic scintillator spectra can be used to reliably identify the bare and unshielded radionuclides that triggered an alarm, even with fewer than 1000 detected counts and in the presence of two or three nuclides at the same time. In this work, we experimentally studied the robustness of a state-of-the-art unmixing algorithm to different radiation background spectra, due to varying atmospheric conditions, in the 16 °C to 28 °C temperature range. In the presence of background, the algorithm is able to identify the nuclides present in unknown radionuclide mixtures of three nuclides, when at least 1000 counts from the sources are detected. With fewer counts available, we found larger differences of approximately 35.9% between estimated nuclide fractions and actual ones. In these low count rate regimes, the uncertainty associated by our algorithm with the identified fractions could be an additional valuable tool to determine whether the identification is reliable or a longer measurement to increase the signal-to-noise ratio is needed. Moreover, the algorithm identification performances are consistent throughout different data sets, with negligible differences in the presence of background types of different intensity and spectral shape.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2017

Detection theory for accurate and non-invasive skin cancer diagnosis using dynamic thermal imaging

Skin cancer is the most common cancer in the United States with over 3.5M annual cases. Presently, visual inspection by a dermatologist has good sensitivity (> 90%) but poor specificity (< 10%), especially for melanoma, which leads to a high number of unnecessary biopsies. Here we use dynamic thermal imaging (DTI) to demonstrate a rapid, accurate and non-invasive imaging system for detection of skin cancer. In DTI, the lesion is cooled down and the thermal recovery is recorded using infrared imaging. The thermal recovery curves of the suspected lesions are then utilized in the context of continuous-time detection theory in order to define an optimal statistical decision rule such that the sensitivity of the algorithm is guaranteed to be at a maximum for every prescribed false-alarm probability. The proposed methodology was tested in a pilot study including 140 human subjects demonstrating a sensitivity in excess of 99% for a prescribed specificity in excess of 99% for detection of skin cancer. To the best of our knowledge, this is the highest reported accuracy for any non-invasive skin cancer diagnosis method.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

False alarms and the IMS infrasound network: Understanding the factors influencing the creation of false events

The International Monitoring System (IMS) infrasound network has been designed to acquire the necessary data to detect and locate explosions in the atmosphere with a yield equivalent to 1 kiloton of TNT anywhere on Earth. A major associated challenge is the task of automatically processing data from all IMS infrasound stations to identify possible nuclear tests for subsequent review by analysts. This paper is the first attempt to quantify the false alarm rate (FAR) of the IMS network, and in particular to assess how the FAR is affected by the numbers and distributions of detections at each infrasound station. To ensure that the results are sufficiently general, and not dependent entirely on one detection algorithm, the assessment is based on two detection algorithms that can be thought of as end members in their approach to the trade-off between missed detections and false alarms. The results show that the FAR for events formed at only two arrays is extremely high (ranging from 10’s to 100’s of false events per day across the IMS network, depending on the detector tuning). It is further shown that the FAR for events formed at three or more IMS arrays is driven by ocean-generated waves (microbaroms), despite efforts within both detection algorithms for avoiding these signals, indicating that further research into this issue is merited. Overall, the results highlight the challenge of processing data from a globally sparse network of stations to detect and form events. On the basis of these results, it is shown that further work on understanding signal and noise characteristics is needed before attempting to automatically form events detected at only two arrays on the global network. In particular, more work is required to reduce false alarms caused by the detection of microbarom signals.

47 OTHER INSTRUMENTATION↗
DOE OSTI2018

VMD fused radar--a hyper-volumetric ultra-low NAR sensor system

A system or method for a physical intrusion detection and assessment system is provided for determining a deliberate motion intrusion. The system includes first and second sensing device to sense physical data set within a predetermined space. A transducer module receives the first sensor physical data set and the second sensor physical data set and converts the first and second physical data sets into analog signals representative of the first and second physical data sets. A signal conditioning layer converts the first and second physical data analog signals to digital signals. A processor device correlates the first physical data set and the second physical data set and generates an alarm in response to identification of an intrusion in the space. A method and algorithms are applied to the data sets for determining whether a detected intrusion is associated with deliberate motion rather than random motion.

42 ENGINEERING↗
DOE OSTI2020

Data for Training and Testing Radiation Detection Algorithms in an Urban Environment

The US government routinely performs radiological response deployments to search for the presence of illicit nuclear materials (e.g., highly enriched uranium and weapons-grade plutonium) in a specified area. The deployments can be intelligence driven, in support of law enforcement, and for planned events such as WrestleMania, presidential inaugurations, or political conventions. In a typical deployment, radiation detection systems carried by human operators or mounted on vehicles move in a clearing pattern through the search area. Search teams rely on radiation detection algorithms running on these systems in real time to alert them to the presence of an illicit threat source. The detection and identification of sources is complicated by large variation of natural radiation background throughout a search area and the potential presence of localized non-threat sources such as patients undergoing treatment with medical isotopes. As a result, detection algorithms must be carefully balanced between missing real sources (false negatives) and reporting too many false alarms (false positives).The purpose of this data set is to spur innovations in detecting, identifying, and localizing nuclear materials inurban search missions.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2021

Infrared intrusion detection system (IRIDS)

A system and method for intrusion detection includes an imager directed towards an object in an interior space. The imager is in data communication with a computer. The computer is arranged to process digital three-dimensional image data received from the imager and programmed to execute a change detection algorithm in response to the processed three-dimensional data to determine movement of the object. The computer generates an alarm output in response to detecting movement of the object above a predetermined threshold. The method includes providing an imager directed towards an object in an interior space; receiving Time of Flight signals by the imager; processing digital three-dimensional image data received from the imager; and executing a change detection algorithm in response to the processed three-dimensional data to determine movement of the object.

Russell, John L.↗
DOE OSTI2022

Environmentally Adaptive, Multiband Software-Defined-Radar for Monitoring of Item of Interest in a Dynamically Cluttered Room

High-resolution multi-frequency radars when networked together, form a robust and seamless monitoring system for high-valued items. The multi-look-direction radar network investigated by LLNL and DSI, operates at 77 GHz and 5.8GHz, and is capable of detection at centimeter resolution movement of objects of interest. This dual frequency radar (unlike optical methods) can operate under various environments with conditions such a smoke, dust, and other natural or manmade obscurations. In addition, the multiband network radar is based on low-cost FCC approved EM specifications and can be scaled with the dimensions of the room and objects under consideration by user-defined inputs. Furthermore, the underlying radar signal processing based on software-defined radar engine is adaptive to its operational environment, where operators can input the size of the facility and object of interest and the distance between the object and radar. Finally, this multi-frequency radar employs a host of change detection and machine learning algorithms to reach performance levels in terms of high probability of detection and low false alarm rates while the monitoring state of the object can be reported remotely.

42 ENGINEERING↗
DOE OSTI2018

Identification of mixed sources with an organic scintillator-based radiation portal monitor

We have developed radionuclide identification algorithms for radiation portal monitor applications. One algorithm uses a spectral angle mapper to match the power spectral density of modified cumulative distribution functions of measured pulse height distributions to reference spectra, while the others rely on the decomposition of the observed spectrum as a linear mixture of known radionuclide spectra. Three algorithms were then tested for their ability to perform on-the-fly radionuclide identification on datasets acquired with a liquid organic scintillator-based pedestrian radiation portal monitor on moving special nuclear material and industrial radiological sources, as well as common medical isotopes. We quantified and compared the relative efficacies of the algorithms considered using F-score analysis. Measured radiation sources included 51 g of highly enriched uranium, 6.6 g of weapons grade plutonium, 241 Am, 133 Ba, 57 Co and 137 Cs sources with activities of several hundred kBq, as well as 260 kBq liquid solution samples of the medical isotopes 99m Tc, 111 In, 67 Ga, 123 I, 131 I, and 201 Tl. We achieved 100% positive identification, for three-second measurements of single sources moving at a source-transit speed of 1.2 m/s. For mixed sources with strongest and weakest sources having no more than a 5:1 ratio of detected counts, encouraging positive identification results were achieved with the un-mixing algorithms. Current radiation portal monitor designs suffer from a high incidence rate of nuisance radiation alarms caused in radiation portal monitor primary screenings by recent nuclear medicine patients and cargo containing large amounts of naturally occurring radioactive materials. Integrating reliable on-the-fly radionuclide identification into the radiation portal monitors could lower the number of nuisance alarms requiring time-consuming secondary inspections.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Can the GPM IMERG Final Product Accurately Represent MCSs’ Precipitation Characteristics over the Central and Eastern United States?

Mesoscale convective systems (MCSs) play an important role in water and energy cycles as they produce heavy rainfall and modify the radiative profile in the tropics and midlatitudes. An accurate representation of MCSs’ rainfall is therefore crucial in understanding their impact on the climate system. The V06B Integrated Multisatellite Retrievals from Global Precipitation Measurement (IMERG) half-hourly precipitation final product is a useful tool to study the precipitation characteristics of MCSs because of its global coverage and fine spatiotemporal resolutions. However, errors and uncertainties in IMERG should be quantified before applying it to hydrology and climate applications. This study evaluates IMERG performance on capturing and detecting MCSs’ precipitation in the central and eastern United States during a 3-yr study period against the radar-based Stage IV product. The tracked MCSs are divided into four seasons and are analyzed separately for both datasets. IMERG shows a wet bias in total precipitation but a dry bias in hourly mean precipitation during all seasons due to the false classification of nonprecipitating pixels as precipitating. These false alarm events are possibly caused by evaporation under the cloud base or the misrepresentation of MCS cold anvil regions as precipitating clouds by the algorithm. IMERG agrees reasonably well with Stage IV in terms of the seasonal spatial distribution and diurnal cycle of MCSs precipitation. A relative humidity (RH)-based correction has been applied to the IMERG precipitation product, which helps reduce the number of false alarm pixels and improves the overall performance of IMERG with respect to Stage IV.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Wide-range fission chambers signal simulator - 373

Fission chambers (FC) signal simulators are a useful means to test neutron flux instrumentation channels of the reactors control and protection systems (CPS). Well-known signal simulators do not provide a secondary equipment testing in all modes of measurement. A wide range simulator that simulates a full range of FC signal changes (pulse, fluctuation, current) was designed. The simulator takes into account the statistical nature of neutrons detection in the FC, including the shape of the pulse and the spectrum of the pulse amplitude distribution. The simulator uses the principle of mathematical simulation of the fission chambers signal with realtime calculations using the FPGA. The wide-range simulator reproduces a FC signal in the range of ten orders without switching ranges or playback modes while ensuring very good linearity. It allows to generate both static and dynamic test signals such as: stair-step, linearly and exponential varying signals, exponential varying signals with a linearly changing period or a signal change simulating the response of the nuclear reactor power to a given reactivity response. Continuity of reproduced signals and the ability to playback dynamic effects makes it possible the simulator using to check the algorithms of cross-linking of measuring paths, reactivity and period measuring tools and to monitor the operation of interlocking and alarm CPS functions. This article discusses the simulator's software and hardware, its features and specifications, user interface and example of the simulator using to check the neutron flux monitoring channels. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2019

Network Detection of Radiation Sources Using Localization-Based Approaches

Radiation source detection is a critical problem in homeland security-related applications. Deploying a network of detectors is expected to provide improved detection due to the combined, albeit dispersed, capture area of multiple detectors. Recently, localization-based detection algorithms provided performance gains beyond the simple “aggregated” area as a result of localization being enabled by the networked detectors. We introduce the following three localization-based detection approaches: 1) source-attractor radiation detection (SRD); 2) triangulation-based radiation source detection (TriRSD); and 3) the ratio of square distance-based radiation source detection (ROSD-RSD). We use canonical datasets from Domestic Nuclear Detection Office's intelligence radiation sensors systems tests to assess the performance of these methods. Extensive results demonstrate that SRD outperforms TriRSD and ROSD-RSD, and other existing detection algorithms based on the sequential probability ratio test and maximum likelihood estimation in terms of both false alarm and detection rates.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗